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176 lines
7.2 KiB
Python
Executable File
176 lines
7.2 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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BF16 最差 30 Case: Primus-Turbo vs CK Grouped GEMM 对比脚本
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从 validation_results.log 提取 CK 测试结果，与 Primus-Turbo 原始数据对比
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"""
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import re
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import sys
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# Primus-Turbo 原始基准数据
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PT_DATA = {
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1: ("DeepSeek-V2-Lite-Down", 2, 512, 2048, 1408, 88.31, 77.83),
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2: ("DeepSeek-V2-Lite-GateUP", 2, 512, 2816, 2048, 128.56, 111.88),
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3: ("DeepSeek-V2-Lite-Down", 4, 512, 2048, 1408, 169.94, 153.80),
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4: ("DeepSeek-V2-Lite-Down", 2, 1024, 2048, 1408, 171.98, 155.41),
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5: ("Mixtral-8x7B-Down", 1, 512, 4096, 14336, 109.59, 235.06),
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6: ("Qwen3-30B-A3B-Down", 4, 512, 2048, 2048, 180.66, 167.65),
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7: ("DeepSeek-V2-Lite-GateUP", 4, 512, 2816, 2048, 235.38, 165.31),
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8: ("Mixtral-8x22B-Down", 1, 512, 6144, 16384, 162.22, 245.13),
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9: ("DeepSeek-V2-Lite-GateUP", 2, 1024, 2816, 2048, 240.26, 185.67),
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10: ("Grok-2-Down", 1, 512, 8192, 16384, 213.48, 249.60),
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11: ("Qwen3-30B-A3B-GateUP", 4, 512, 4096, 2048, 302.33, 181.72),
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12: ("DeepSeek-V2-Lite-Down", 8, 512, 2048, 1408, 274.33, 212.22),
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13: ("Qwen3-30B-A3B-Down", 8, 512, 2048, 2048, 289.45, 219.50),
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14: ("DeepSeek-V2-Lite-Down", 4, 1024, 2048, 1408, 282.12, 232.32),
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15: ("Mixtral-8x7B-Down", 1, 1024, 4096, 14336, 212.73, 337.73),
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16: ("Qwen3-30B-A3B-Down", 4, 1024, 2048, 2048, 297.60, 253.24),
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17: ("DeepSeek-V2-Lite-Down", 2, 2048, 2048, 1408, 293.07, 262.01),
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18: ("DeepSeek-V2-Down", 5, 512, 5120, 1536, 378.06, 180.62),
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19: ("Qwen3-235B-A22B-Down", 4, 512, 4096, 4096, 330.68, 239.15),
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20: ("DeepSeek-V2-Lite-GateUP", 8, 512, 2816, 2048, 350.89, 223.60),
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21: ("DeepSeek-V2-GateUP", 5, 512, 3072, 5120, 310.56, 265.50),
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22: ("DeepSeek-V2-Down", 10, 512, 5120, 1536, 354.81, 238.12),
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23: ("Mixtral-8x7B-GateUP", 1, 512, 28672, 4096, 449.17, 144.20),
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24: ("DeepSeek-V2-Lite-GateUP", 4, 1024, 2816, 2048, 364.11, 241.49),
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25: ("Mixtral-8x22B-GateUP", 1, 512, 32768, 6144, 457.38, 179.06),
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26: ("Mixtral-8x22B-Down", 1, 1024, 6144, 16384, 292.92, 346.51),
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27: ("DeepSeek-V2-Lite-Down", 8, 1024, 2048, 1408, 395.28, 245.58),
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28: ("DeepSeek-V2-Down", 5, 1024, 5120, 1536, 367.96, 276.79),
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29: ("DeepSeek-V2-Lite-GateUP", 2, 2048, 2816, 2048, 376.12, 270.01),
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30: ("DeepSeek-V2-Lite-GateUP", 8, 1024, 2816, 2048, 337.25, 310.81),
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}
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def parse_log(log_file):
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"""解析 validation_results.log 提取 CK TFLOPS 数据"""
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with open(log_file, 'r') as f:
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content = f.read()
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# 提取所有 TFlops 值
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tflops_pattern = r'Perf:.*?(\d+\.?\d*) TFlops'
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tflops_values = [float(x) for x in re.findall(tflops_pattern, content)]
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# 每个 rank 有 3 个值: Forward, Backward_A, Backward_B
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ck_data = {}
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for rank in range(1, 31):
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idx = (rank - 1) * 3
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if idx + 2 < len(tflops_values):
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ck_data[rank] = (
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tflops_values[idx], # Forward
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tflops_values[idx + 1], # Backward grad_A
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tflops_values[idx + 2], # Backward grad_B
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)
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# 统计信息
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correct_count = content.count("correct")
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fail_count = content.count("fail")
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tile_256 = content.count("256x256 tile")
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tile_128 = content.count("256x128 tile")
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return ck_data, correct_count, fail_count, tile_256, tile_128
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def harmonic_mean(a, b):
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"""计算调和平均: 2 / (1/a + 1/b)
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这是正确的方式来合并两个 TFLOPS 值，因为:
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Combined_TFLOPS = Total_FLOPs / Total_Time
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= (FLOPs_A + FLOPs_B) / (Time_A + Time_B)
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= 2*FLOPs / (FLOPs/TFLOPS_A + FLOPs/TFLOPS_B) (当 FLOPs_A = FLOPs_B 时)
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= 2 / (1/TFLOPS_A + 1/TFLOPS_B)
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"""
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if a <= 0 or b <= 0:
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return 0.0
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return 2.0 / (1.0/a + 1.0/b)
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def print_comparison(ck_data):
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"""打印对比表格"""
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sep = "=" * 195
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line = "-" * 195
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print(sep)
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print("BF16 最差 30 Case: Primus-Turbo vs CK Grouped GEMM 完整对比")
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print(sep)
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print(f"{'Rank':<5} {'Case':<28} {'B':<3} {'M':<5} {'N':<6} {'K':<6} | {'CK_Fwd':>8} {'CK_BwdA':>8} {'CK_BwdB':>8} {'CK_Bwd':>8} | {'PT_Fwd':>8} {'PT_Bwd':>8} | {'Δ Fwd':>8} {'Δ Bwd':>8}")
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print(line)
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total_ck_fwd = total_ck_bwd = 0
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total_pt_fwd = total_pt_bwd = 0
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for rank in range(1, 31):
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case, B, M, N, K, pt_fwd, pt_bwd = PT_DATA[rank]
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if rank in ck_data:
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ck_fwd, ck_bwd_a, ck_bwd_b = ck_data[rank]
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else:
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ck_fwd = ck_bwd_a = ck_bwd_b = 0.0
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# 使用调和平均计算综合 backward TFLOPS (正确的合并方式)
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ck_bwd_combined = harmonic_mean(ck_bwd_a, ck_bwd_b)
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delta_fwd = ck_fwd - pt_fwd
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delta_bwd = ck_bwd_combined - pt_bwd
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total_ck_fwd += ck_fwd
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total_ck_bwd += ck_bwd_combined
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total_pt_fwd += pt_fwd
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total_pt_bwd += pt_bwd
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print(f"{rank:<5} {case:<28} {B:<3} {M:<5} {N:<6} {K:<6} | {ck_fwd:>8.2f} {ck_bwd_a:>8.2f} {ck_bwd_b:>8.2f} {ck_bwd_combined:>8.2f} | {pt_fwd:>8.2f} {pt_bwd:>8.2f} | {delta_fwd:>+8.2f} {delta_bwd:>+8.2f}")
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print(line)
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avg_ck_fwd = total_ck_fwd / 30
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avg_ck_bwd = total_ck_bwd / 30
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avg_pt_fwd = total_pt_fwd / 30
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avg_pt_bwd = total_pt_bwd / 30
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print(f"{'平均':<38} | {avg_ck_fwd:>8.2f} {avg_ck_bwd:>26.2f} | {avg_pt_fwd:>8.2f} {avg_pt_bwd:>8.2f} | {avg_ck_fwd-avg_pt_fwd:>+8.2f} {avg_ck_bwd-avg_pt_bwd:>+8.2f}")
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print(sep)
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return avg_ck_fwd, avg_ck_bwd, avg_pt_fwd, avg_pt_bwd
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def print_summary(avg_ck_fwd, avg_ck_bwd, avg_pt_fwd, avg_pt_bwd,
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correct_count, fail_count, tile_256, tile_128):
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"""打印总结"""
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sep = "=" * 100
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print()
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print(sep)
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print("性能对比总结")
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print(sep)
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print(f"Forward 平均: PT = {avg_pt_fwd:.2f} TFLOPS → CK = {avg_ck_fwd:.2f} TFLOPS ({(avg_ck_fwd/avg_pt_fwd-1)*100:>+.1f}%)")
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print(f"Backward 平均: PT = {avg_pt_bwd:.2f} TFLOPS → CK = {avg_ck_bwd:.2f} TFLOPS ({(avg_ck_bwd/avg_pt_bwd-1)*100:>+.1f}%)")
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avg_pt = (avg_pt_fwd + avg_pt_bwd) / 2
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avg_ck = (avg_ck_fwd + avg_ck_bwd) / 2
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print(f"综合平均: PT = {avg_pt:.2f} TFLOPS → CK = {avg_ck:.2f} TFLOPS ({(avg_ck/avg_pt-1)*100:>+.1f}%)")
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print(sep)
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print()
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print(f"精度验证: {correct_count} 通过, {fail_count} 失败")
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print(f"Tile 配置: 256x256 使用 {tile_256} 次, 256x128 使用 {tile_128} 次")
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def main():
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log_file = sys.argv[1] if len(sys.argv) > 1 else "validation_results.log"
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try:
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ck_data, correct_count, fail_count, tile_256, tile_128 = parse_log(log_file)
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except FileNotFoundError:
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print(f"错误: 找不到文件 {log_file}")
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print("用法: python3 compare_results.py [validation_results.log]")
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sys.exit(1)
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avg_ck_fwd, avg_ck_bwd, avg_pt_fwd, avg_pt_bwd = print_comparison(ck_data)
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print_summary(avg_ck_fwd, avg_ck_bwd, avg_pt_fwd, avg_pt_bwd,
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correct_count, fail_count, tile_256, tile_128)
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if __name__ == "__main__":
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main()
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